OpenAIR Community:http://hdl.handle.net/10059/2
Tue, 24 Feb 2015 14:32:11 GMT2015-02-24T14:32:11ZCloud computing: adoption issues for sub-Saharan African SMEs.http://hdl.handle.net/10059/1147
Title: Cloud computing: adoption issues for sub-Saharan African SMEs.
Authors: Dahiru, Abubakar Abubakar; Bass, Julian M.; Allison, Ian K.
Abstract: This paper explores the emergence and adoption of cloud computing by small and medium-sized enterprises (SMEs) and points towards its implications for developing countries in sub-Saharan Africa. Several studies have shown the importance of technologies to SMEs and the potentials of SMEs for economic growth. Using qualitative techniques we obtained and analysed data from ten SMEs that have adopted cloud computing as an IT strategy. These SMEs span across various sectors including finance, information and communication technology (ICT), and manufacturing in Nigeria, a developing country in sub-Saharan Africa. We found that, contrary to the literature on cloud computing adoption in the global north, these SMEs are less concerned with challenges like security, privacy and data loss rather; they continue to show optimism in using the potential opportunities that cloud computing presents to them. We envisage that as cloud computing evolves, more SMEs in sub-Saharan Africa will adopt it as an IT Strategy. This could positively contribute to the successes of these SMEs and consequently contribute to the economic growth desired by these developing countries.Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10059/11472014-01-01T00:00:00ZAnomaly monitoring framework based on intelligent data analysis.http://hdl.handle.net/10059/1146
Title: Anomaly monitoring framework based on intelligent data analysis.
Authors: Rattadilok, Prapa; Petrovski, Andrei; Petrovski, Sergei
Abstract: Real-time data processing has become an increasingly important challenge as the need for faster analysis of big data widely manifests itself. In this research, several methods of Computational Intelligence have been applied for identifying possible anomalies in two datasets. The proposed framework shows a promising potential for anomaly detection and its lightweight, real-time features make it applicable to a range of in-situ data analysis scenarios.Tue, 01 Oct 2013 00:00:00 GMThttp://hdl.handle.net/10059/11462013-10-01T00:00:00ZInferential measurements for situation awareness: enhancing traffic surveillance by machine learning.http://hdl.handle.net/10059/1145
Title: Inferential measurements for situation awareness: enhancing traffic surveillance by machine learning.
Authors: Rattadilok, Prapa; Petrovski, Andrei
Abstract: The paper proposes a generic approach to building inferential measurement systems. The large amount of data needed to be acquired and processed by such systems necessitates the use of machine learning techniques. In this study, an inferential measurement system aimed at enhancing situation awareness has been developed and tested on simulated traffic surveillance data. The performance of several Computational Intelligence techniques within this system has been examined and compared on the data containing anomalous driving patterns.Mon, 01 Jul 2013 00:00:00 GMThttp://hdl.handle.net/10059/11452013-07-01T00:00:00ZSelf-learning data processing framework based on computational intelligence: enhancing autonomous control by machine intelligence.http://hdl.handle.net/10059/1144
Title: Self-learning data processing framework based on computational intelligence: enhancing autonomous control by machine intelligence.
Authors: Rattadilok, Prapa; Petrovski, Andrei
Abstract: A generic framework for evolving and autonomously controlled systems has been developed and evaluated in this paper. A three-phase approach aimed at identification, classification of anomalous data and at prediction of its consequences is applied to processing sensory inputs from multiple data sources. An ad-hoc activation of sensors and processing of data minimises the quantity of data that needs to be analysed at any one time. Adaptability and autonomy are achieved through the combined use of statistical analysis, computational intelligence and clustering techniques. A genetic algorithm is used to optimise the choice of data sources, the type and characteristics of the analysis undertaken. The experimental results have demonstrated that the framework is generally applicable to various problem domains and reasonable performance is achieved in terms of computational intelligence accuracy rate. Online learning can also be used to dynamically adapt the system in near real time.Mon, 01 Dec 2014 00:00:00 GMThttp://hdl.handle.net/10059/11442014-12-01T00:00:00Z